Antenna Season Report Notebook

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder

# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "223"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"

display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 223 Report

import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna

# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 255 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 233 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect

# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs

this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/anaconda/envs/RTP/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3360             try:
-> 3361                 return self._engine.get_loc(casted_key)
   3362             except KeyError as err:

~/anaconda/envs/RTP/lib/python3.7/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

~/anaconda/envs/RTP/lib/python3.7/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Ant'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_17221/3863632283.py in <module>
      8         # Add this day to the antenna
      9         row = df.loc[n]
---> 10         antnum = row['Ant']
     11         if antnum != int(antenna):
     12             continue

~/anaconda/envs/RTP/lib/python3.7/site-packages/pandas/core/series.py in __getitem__(self, key)
    940 
    941         elif key_is_scalar:
--> 942             return self._get_value(key)
    943 
    944         if is_hashable(key):

~/anaconda/envs/RTP/lib/python3.7/site-packages/pandas/core/series.py in _get_value(self, label, takeable)
   1049 
   1050         # Similar to Index.get_value, but we do not fall back to positional
-> 1051         loc = self.index.get_loc(label)
   1052         return self.index._get_values_for_loc(self, loc, label)
   1053 

~/anaconda/envs/RTP/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3361                 return self._engine.get_loc(casted_key)
   3362             except KeyError as err:
-> 3363                 raise KeyError(key) from err
   3364 
   3365         if is_scalar(key) and isna(key) and not self.hasnans:

KeyError: 'Ant'

# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])]) 

Table 1: Per-Night RTP Summary Info For This Atenna

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_17221/880101968.py in <module>
----> 1 display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
      2 HTML(table.render(render_links=True, escape=False))

AttributeError: 'NoneType' object has no attribute 'node'

Load antenna metric spectra and waterfalls from auto_metrics notebooks.

htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 223: 2459796

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	ee Power	10.547586	6.720804	3.895193	10.547586	4.896520	7.030542	6.627331	1.320874	2.036498

---

Antenna 223: 2459795

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Power	13.949159	12.957537	7.190175	13.949159	7.802564	13.386302	7.905581	0.466349	-0.741346

---

Antenna 223: 2459794

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	16.895872	6.768655	12.711286	8.248792	15.114315	9.628099	16.895872	-0.973816	-0.263358

---

Antenna 223: 2459793

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	17.197273	5.376617	9.923256	6.971679	13.813600	8.906011	17.197273	-0.785137	-0.918298

---

Antenna 223: 2459792

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	18.744412	5.494609	9.575804	8.350783	16.921038	9.173801	18.744412	0.966683	-0.640215

---

Antenna 223: 2459791

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Power	19.471790	6.402181	11.822578	10.389915	19.471790	11.105470	18.620071	0.843922	0.071399

---

Antenna 223: 2459790

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	26.067295	18.932280	10.290733	18.386628	9.534865	26.067295	14.436727	-1.324599	-0.965936

---

Antenna 223: 2459789

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	26.616023	16.435703	8.576212	19.495763	9.617245	26.616023	15.508732	0.689170	-0.037500

---

Antenna 223: 2459788

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	28.423850	14.100639	7.286802	20.747616	10.057551	28.423850	16.113036	-1.336583	-0.996385

---

Antenna 223: 2459787

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	17.702029	6.915084	13.096680	8.161828	17.084515	10.092496	17.702029	-0.827780	-1.707864

---

Antenna 223: 2459786

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	26.324033	13.619314	7.548689	19.991449	10.432403	26.324033	15.996743	-1.450823	-0.984033

---

Antenna 223: 2459785

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Power	41.316336	12.199116	4.503389	41.316336	14.434731	34.202562	11.096039	-2.188371	-0.552386

---

Antenna 223: 2459784

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	38.535917	6.922904	18.790413	11.709987	32.589113	15.655944	38.535917	0.824953	-1.367187

---

Antenna 223: 2459783

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	17.490417	15.800590	13.077605	8.854528	7.587577	17.490417	16.017614	-1.579207	-0.943821

---

Antenna 223: 2459782

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Shape	14.404894	14.404894	12.075752	4.343363	3.849737	9.797308	8.868729	-1.497006	-1.347666

---

Antenna 223: 2459781

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	2.334512	1.395588	1.158582	0.381350	0.426777	-1.803530	2.334512	-1.846811	-1.539845

---

Antenna 223: 2459778

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	nn Shape Modified Z-Score	ee Shape Modified Z-Score	nn Power Modified Z-Score	ee Power Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	ee Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Variability	23.700925	6.731779	7.581398	4.861504	5.679054	23.700925	13.897115	9.116955	-2.967968

---

Antenna 223: 2459776

Ant	Node	A Priori Status	Worst Metric	Worst Modified Z-Score	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score
223	N19	RF_ok	nn Temporal Discontinuties	54.820456	6.027237	3.417068	6.123575	4.796983	18.054501	41.516143	21.554546	54.820456

---